1. Field of the Invention
The present invention relates to systems that employ an electronic program guide to assist a media user in managing a large number of media-content choices (e.g., television programming, chatrooms, on-demand video media files, audio, etc.). The present invention more specifically relates to systems having the “intelligence” to suggest choices to a user and to take actions based on the suggestions (e.g., record a program on behalf of the user).
2. Description of the Related Art
As the number of channels available to television viewers has increased, along with the diversity of the programming content available on such channels, it has become increasingly challenging for television viewers to identify television programs of interest. Historically, television viewers identified television programs of interest by analyzing printed television program guides. Typically, such printed television program guides contained grids listing the available television programs by time and date, channel and title. As the number of television programs has increased, the ability to effectively identify desirable television programs using such printed guides has become impractical.
More recently, television program guides have become available in an electronic format, often referred to as electronic program guides (EPGs). Like printed television program guides, EPGs contain grids listing the available television programs by time, date, channel and title. An EPG, however, allows television viewers to sort or search the available television programs in accordance with personalized preferences. In addition, EPGs allow for on-screen presentation of the available television programs.
While EPGs allow viewers to identify several desirable programs more efficiently than conventional printed guides, they suffer from a number of limitations, which if overcome, could further enhance the ability of viewers to identify desirable programs. For example, many viewers have a particular preference towards, or bias against, certain categories of programming, such as action-based programs, or sports programming. Thus, the viewer preferences can be applied to the EPG to obtain a set of recommended programs that may be of interest to a particular viewer.
The ultimate goal in the design of a television program recommendation program is to achieve the best possible classification of programs. This objective led to a development of different classifiers modules such as a Bayesian classifier module and a decision tree classifier module. A Bayesian classifier module is parametric, and consequently, numerous assumptions have to be made about the programs. A decision tree classifier module involves intensive computations that tend to produce a large number of disjoint rules when the data is sparse. The present invention addresses these specific disadvantages of prior art classifiers modules for recommending programs.